Poly-substance use, treatment completion, and contact with the justice system: a multistate analysis of treatments for substance use disorders between 2010-2019 in Chile

A. González-Santa Cruz1, 2, , J. Ruiz-Tagle Maturana1, 3, , M. Mateo Piñones1, 4, , A. Castillo-Carniglia5, 6,


1 Young Researcher, Millennium Nucleus for the evaluation and analysis of Drug Policies
2 Ph.D. student, School of Public Health, Universidad de Chile
3 Ph.D. student, Programa de Doctorado en Políticas Públicas, Universidad Mayor, Santiago, Chile.
4 Ph.D. student, Griffith University, Australia
5 Director, Millennium Nucleus for the evaluation and analysis of Drug Policies
6 Associate Professor, Society & Health Research Center

Background

Research has shown that reducing SUDs through effective treatment leads to a reduction in criminal activity\(^{[1]}\). However, most evidence comes from developed countries, and results from the Latin American context are largely unknown\(^{[2]}\). The structural, economic, epidemiological context and substance use treatment (SUT) policy response are different in this region, making the question about SUT effectiveness through locally based data relevant\(^{[3]}\).

Objectives

We analyze Chile as a case study and examine the impact of SUT on the prevention of contact with the criminal justice system (CJS) in the short (3 and 6 months), middle (1 year), and long term (3 years). Hypothesis: Patients who complete treatment have lower probabilities of being in contact with CJS compared to patients who do not complete, although this effect may decrease as observation time passes.

Methods

This research relies on a population-based record-linkage retrospective cohort design. We used a deterministic linkage process (using encryption of the Chilean Unique National ID) to merge electronic records of individuals in publicly funded Chilean SUT programs with the Prosecutor’s Office data at the national level between 2010 and 2019. This research is approved by the Griffith University Human Research Ethics Committee (GUHREC) (GU Ref No: 2022/919).

We described the cumulative incidence rate and incidence rate ratio (IRR) of contact with the CJS (offenses that ended with a condemnatory sentence and of offenses that ended with imprisonment after baseline treatment outcome), and its variation by baseline treatment outcome: Treatment completion, Late (>= 3months) & Early Discharge (within the first 3 months of treatment). We calculated the association between Baseline treatment outcome and Contact with CJS through Royston-Parmar models while adjusting for several covariates and obtained standardized survival curves and restricted mean survival times (RMST) through the stpm2 command in Stata\(^{[4]}\). Missing data was imputed using multiple imputation with regression trees from missRanger R package\(^{[5]}\). Secondary analyses included e-values of the strength of confounding needed to take away the associations between treatment outcome and contact with CJS. Codes are available at bit.ly/40cMATs. Covariates are listed below:

  • Treatment non-completion (Early)
  • Treatment non-completion (Late)
  • Treatment setting
  • Sex
  • Substance use onset age
  • Educational attainment
  • Primary substance at admission
  • Primary substance at admission usage frequency
  • Occupational status
  • Poly-substance use
  • Number of children (binary)
  • Tenure status of households
  • Macrozone
  • Number of previous offenses (violent)
  • Number of previous offenses (acquisitive)
  • Number of previous offenses (SUD)
  • Number of previous offenses (other)
  • Psychiatric comorbidity
  • Substance use severity
  • Urban/rural municipality of residence
  • Percentage of poverty of the municipality of residence
  • Substance use onset
  • Treatment admission year
  • Cohabitation status
  • Physical comorbidity
  • Age
  • Preliminary Results

    Of the 109,756(p= 85,048) SENDA records of admissions, 70,863(83%) were eligible to be matched with the Prossecutor’s Office database (discarded ongoing treatments or treatments that ended in referrals). 22,287(31%) had at least an offense that ended with a condemnatory sentence after baseline treatment. Those that had at least an offense that ended with imprisonment were 5,144(7%).
    Table 1: Offending with Condemnatory Sentence
    Time Complete Tr. Late Disch. Early Disch. Comp. vs Late Comp. vs Early Early vs Late
    Probs.
    3_mths 96.9 (96.7,97.1) 94.7 (94.5,94.9) 93.8 (93.5,94.1) -2.2 (-2.5,-1.9) -3.1 (-3.4,-2.7) .9 (.5,1.2)
    6_mths 94.3 (94,94.6) 90.6 (90.4,90.9) 89.3 (88.9,89.8) -3.6 (-4,-3.3) -4.9 (-5.4,-4.4) 1.3 (.8,1.8)
    1_yr 90 (89.6,90.4) 84.4 (84.1,84.8) 82.8 (82.3,83.3) -5.5 (-6,-5) -7.2 (-7.9,-6.5) 1.7 (1,2.3)
    3_yrs 79.4 (78.8,80) 70.9 (70.4,71.3) 69 (68.3,69.7) -8.6 (-9.3,-7.9) -10.4 (-11.4,-9.5) 1.9 (1,2.7)
    5_yrs 73.4 (72.7,74.2) 63.9 (63.4,64.5) 62.2 (61.4,63.1) -9.5 (-10.3,-8.7) -11.2 (-12.3,-10.1) 1.7 (.8,2.7)
    RMST
    3_mths .251 (.25,.251) .247 (.247,.248) .246 (.246,.247) -.002 (-.002,-.002) -.003 (-.003,-.002) .001 (.001,.001)
    6_mths .494 (.493,.495) .483 (.482,.484) .479 (.478,.48) -.011 (-.012,-.01) -.015 (-.017,-.013) .004 (.003,.006)
    1_yr .962 (.959,.965) .928 (.925,.93) .916 (.912,.92) -.034 (-.038,-.031) -.046 (-.051,-.041) .012 (.007,.016)
    3_yrs 2.622 (2.61,2.635) 2.442 (2.433,2.452) 2.394 (2.379,2.41) -.18 (-.195,-.164) -.228 (-.248,-.207) .048 (.029,.067)
    5_yrs 4.172 (4.148,4.197) 3.807 (3.788,3.825) 3.722 (3.692,3.752) -.366 (-.395,-.336) -.45 (-.491,-.41) .085 (.049,.121)

    Table 2: Offending with imprisonment
    Time Complete Tr. Late Disch. Early Disch. Comp. vs Late Comp. vs Early Early vs Late
    Probs.
    3_mths 99.5 (99.5,99.6) 99.1 (99,99.2) 98.8 (98.7,98.9) -.5 (-.6,-.4) -.7 (-.9,-.6) .3 (.1,.4)
    6_mths 99.1 (99,99.3) 98.4 (98.3,98.5) 98 (97.8,98.1) -.8 (-.9,-.6) -1.2 (-1.4,-1) .4 (.2,.6)
    1_yr 98.4 (98.3,98.6) 97.2 (97.1,97.4) 96.6 (96.3,96.8) -1.2 (-1.4,-1) -1.9 (-2.2,-1.5) .6 (.4,.9)
    3_yrs 96.4 (96.1,96.7) 94.3 (94,94.5) 93.2 (92.8,93.6) -2.2 (-2.5,-1.8) -3.2 (-3.7,-2.8) 1.1 (.6,1.5)
    5_yrs 94.9 (94.5,95.3) 92.3 (92,92.6) 91 (90.5,91.4) -2.6 (-3.1,-2.2) -4 (-4.6,-3.3) 1.3 (.8,1.9)
    RMST
    3_mths .254 (.254,.254) .253 (.253,.253) .253 (.253,.253) -.001 (-.001,0) -.001 (-.001,-.001) 0 (0,.001)
    6_mths .507 (.506,.507) .505 (.504,.505) .503 (.503,.504) -.002 (-.003,-.002) -.003 (-.004,-.003) .001 (.001,.002)
    1_yr 1.01 (1.008,1.011) 1.002 (1.001,1.003) .998 (.997,1) -.007 (-.009,-.006) -.011 (-.013,-.009) .004 (.002,.006)
    3_yrs 2.931 (2.925,2.936) 2.889 (2.884,2.893) 2.867 (2.86,2.875) -.042 (-.049,-.035) -.063 (-.073,-.054) .022 (.013,.031)
    5_yrs 4.878 (4.865,4.89) 4.786 (4.777,4.796) 4.74 (4.724,4.755) -.091 (-.107,-.076) -.138 (-.158,-.117) .046 (.027,.065)
    • Compared to those receiving almost no treatment (early drop-out), those completing SUT took longer to contact the criminal justice system (IRR [Incidence rate ratio]= 2.18 95% CI 2.09,2.27; aHR[adjusted hazard ratio]: 1.74 95%CI 1.66, 1.83) and to commit an offence leading to imprisonment 2.90 (95% CI 2.64,3.18; aHR= 1.99 95%CI 1.79, 2.22).
    • Compared to receiving some treatment (late drop-out), those completing SUT took longer (IRR= 1.73 95% CI 1.67,1.80; aHR=1.58 95%CI 1.52, 1.65) to contact the criminal justice system and to imprisonment (IRR= 1.93 95% CI 1.77,2.10; aHR=1.65 95%CI 1.51, 1.81).
    • However, the difference was lower when we compared those who received some treatment with those with less SUT for some period (late drop-out) regarding the time to contact the criminal justice system (IRR= 1.26 95% CI 1.22,1.30) and imprisonment (IRR= 1.50 95% CI 1.41,1.61). Differences between Early vs Late did not overlap the null in Tables 1 & 2.

    • Condemnatory Sentence: E-value of at least 2.19 for Early and 2.01 for Late discharge vs. treatment completion.
    • Imprisonment: E-value of at least 2.36 for Early and 1.99 for Late discharge vs. treatment completion.

    Discussion

    References

    [1] M. Prendergast, D. Podus, E. Chang, et al. “Erratum to The effectiveness of drug abuse treatment: a meta-analysis of comparison group studies”. In: Drug and Alcohol Dependence - DRUG ALCOHOL DEPENDENCE 84 (sept.. 2006), pp. 133-133. DOI: 10.1016/j.drugalcdep.2006.02.002.

    [2] H. Klingemann. “Successes and Failures in Treatment of Substance Abuse: Treatment System Perspectives and Lessons from the European Continent”. In: Nordisk Alkohol- and Narkotikatidskrift 37.4 (2020), pp. 323-37.

    [3] M. Mateo Pinones, A. González-Santa Cruz, R. Portilla Huidobro, et al. “Evidence-based policymaking: Lessons from the Chilean Substance Use Treatment Policy”. En. In: Int. J. Drug Policy 109.103860 (nov.. 2022), p. 103860.

    [4] P. Lambert. STPM2: Stata module to estimate flexible parametric survival models. Statistical Software Components, Boston College Department of Economics. feb.. 2010. URL: https://ideas.repec.org/c/boc/bocode/s457128.html.

    [5] M. Mayer. “missRanger: Fast Imputation of Missing Values”. (2023). R package version 2.2.0. URL: https://github.com/mayer79/missRanger.

    Funding sources

    • This study is funded by the Millennium Nucleus for the Evaluation and Analysis of Drug Policies (nDP), Chile to Dr. Álvaro Castillo-Carniglia; The author have no conflict of interest to declare
    • Correspondence to: Andrés González-Santa Cruz,